CaFNet: A Confidence-Driven Framework for Radar Camera Depth Estimation

TL;DR

CaFNet is a novel confidence-driven framework that fuses radar and camera data for more accurate and robust depth estimation in autonomous driving, effectively handling radar noise and ambiguities.

Contribution

The paper introduces a two-stage, end-to-end trainable network with a confidence-aware fusion mechanism for radar-camera depth estimation, including a new method for generating radar confidence ground truth.

Findings

Achieves 3.2% improvement in MAE over previous models

Attains 2.7% reduction in RMSE, demonstrating enhanced accuracy

Effectively filters radar noise through confidence-aware fusion mechanism

Abstract

Depth estimation is critical in autonomous driving for interpreting 3D scenes accurately. Recently, radar-camera depth estimation has become of sufficient interest due to the robustness and low-cost properties of radar. Thus, this paper introduces a two-stage, end-to-end trainable Confidence-aware Fusion Net (CaFNet) for dense depth estimation, combining RGB imagery with sparse and noisy radar point cloud data. The first stage addresses radar-specific challenges, such as ambiguous elevation and noisy measurements, by predicting a radar confidence map and a preliminary coarse depth map. A novel approach is presented for generating the ground truth for the confidence map, which involves associating each radar point with its corresponding object to identify potential projection surfaces. These maps, together with the initial radar input, are processed by a second encoder. For the final depth estimation, we innovate a confidence-aware gated fusion mechanism to integrate radar and image features effectively, thereby enhancing the reliability of the depth map by filtering out radar noise. Our methodology, evaluated on the nuScenes dataset, demonstrates superior performance, improving upon the current leading model by 3.2% in Mean Absolute Error (MAE) and 2.7% in Root Mean Square Error (RMSE). Code: https://github.com/harborsarah/CaFNet